Hypothesis and experimentation

The first conclusion is limited to the experimental data. Avoid words such as always and certainly without sufficient evidence.

Support means that available evidence is consistent with the hypothesis. Future evidence may change the conclusion.

Measurable variables make a hypothesis testable through experiment. Replace vague words with clear outcomes.

The direct answer is B, sampling bias. Sampling means choosing a smaller group to represent a larger group. A fair sample should give different plants a reasonable and equal chance of being selected. Here the student chooses only the tallest plant. Height may already be affected by soil, water, light, age, or natural differences, so this one plant cannot represent all plants. Step by step: the whole group is all available plants; the chosen sample is only the tallest one; the sample is not typical or fairly selected; therefore the conclusion about fertiliser may be misleading. A, random error, means unpredictable variation in measurements or results, not unfair selection. B is correct because the selection rule favours the tallest plants. C, proper control, means keeping other conditions the same or using a suitable comparison group; no such method is shown. D, proper repetition, means repeating trials or using several samples; selecting one plant is the opposite. Memory cue: unfair sample selection means sampling bias.

Safety instructions are designed to prevent harm while an experiment is being performed. Laboratories may contain heat, glass, chemicals, electricity or equipment that can cause danger if handled carelessly. Following precautions, using suitable protective equipment and listening to instructions protect the experimenter and nearby people. Safety is more important than obtaining a result quickly.

Option B is correct because ignoring precautions can cause an injury or an accident, which is the most serious consequence listed. The amount of data does not automatically increase, and a hypothesis cannot become longer merely because safety rules were ignored. A controlled variable is a condition kept the same, not something that increases because of unsafe behaviour. Therefore, the main reason for following instructions is prevention of physical harm and damage.

Improved plant condition is vague and does not specify a definite changed factor. State the independent variable measurably.

A table arranges results in rows and columns, making it easier to compare observations or measurements and identify patterns. It does not automatically prove a hypothesis or eliminate measurement errors. Exam tip: always include clear column headings and correct units of measurement.

Different initial heights may confuse comparison of final height. Use similar plants or compare height gain.

Similar results obtained by another person using the same method demonstrate reproducibility. Record the procedure clearly.

Changing the amount of sugar can change the time needed for it to dissolve. Therefore, it would be unclear whether a difference in the result is caused by the water temperature or by the amount of sugar. The sugar amount is thus a confounding variable and should be kept the same in every trial. Exam tip: In a fair test, keep all relevant variables constant except the independent variable.

A conclusion should be supported by enough reliable evidence. One trial that produces an unusual result may be affected by random error, an instrument problem, or an unnoticed change in conditions. It is therefore unsafe to treat that single observation as a general pattern. The experiment should be repeated, the procedure checked, and more data collected before making a firm conclusion. Hence option A is correct.

Several similar results from repeated trials provide stronger evidence, especially when the experiment is controlled and all measurements are recorded. Keeping controlled variables the same also helps make the comparison fair. Recording all data is necessary for evaluation, although recording alone does not guarantee a correct conclusion. A repeated unusual result should not simply be ignored; it should be investigated and reported honestly, but one unusual trial by itself is insufficient.

In option A, temperature is deliberately changed, so it is the independent variable. The time taken by wax to melt is measured as a result of that change; therefore, time is the dependent variable. In option C, changing mass is an action in the procedure, not the measured outcome. Exam tip: the variable recorded as the result of an experimental change is usually the dependent variable.

The correct answer is option A: an initial measurement helps determine the actual change. The first measurement is called the baseline or starting value. After the experiment or treatment, a final measurement is taken. The change is found by subtracting the initial value from the final value: change = final measurement − initial measurement. For example, if a plant is 10 cm initially and 14 cm later, its increase is 14−10=4 cm. Without the starting value, we know only the final condition, not how much it changed. Option B is wrong because a sound experiment does not decide its result in advance; the evidence should determine the conclusion. Option C is wrong because an initial measurement does not remove a control group; controls help provide a comparison. Option D is wrong because the baseline makes the investigation clearer and does not hide the hypothesis. The exam cue is to compare before and after, not only the final reading.

The direct answer is A. A testable prediction states what measurable change should happen when one condition changes. In option A, the condition is temperature, the amount of ice is kept equal, and the predicted result is a shorter melting time at higher temperature. A student can test it by using equal ice samples, different controlled temperatures, measuring time with a stopwatch, and comparing the results. Option B, “heat is good,” is vague and gives no measurable quantity or clear test. Option C, “ice is beautiful,” is a personal opinion rather than a precise scientific prediction. Option D, “cold is the best weather,” is also an opinion and does not identify a measurement. Therefore A is correct because it connects variables in a way an experiment can check. Exam cue: a testable statement must tell what changes and what can be measured.

Direct answer: A, check the procedure and controlled variables. Repeatedly changing results suggest that the experiment is not being performed consistently, an instrument may be unreliable, or another variable is changing. Step by step: write the method clearly, keep temperature, amount, time, measuring position, and other relevant conditions constant, check the instrument, repeat trials, and record every result. A is correct because it investigates sources of variation before judging the hypothesis. B selects evidence by preference and creates bias. C declares success without sufficient evidence. D deletes inconvenient data and is scientifically dishonest. Memory cue: inconsistent results require control and checking, not selective reporting.

An experiment is valid when its design allows a fair conclusion about the factor being tested. This requires changing the independent variable while keeping other important factors constant. If two factors change at the same time, a result may be caused by either one, or by both together. The experiment then cannot show clearly which factor produced the observed effect.

Therefore option A creates the greatest problem for validity. For example, if a study tests fertiliser but also changes the amount of water, any growth difference cannot be attributed confidently to fertiliser alone. Using the same unit, repeating trials, and recording all results generally improve the quality of an investigation. They do not create the confounding problem described in option A.

Changing only water amount for identical seeds provides a fair comparison. Germination percentage is a measurable outcome.

Date and time show when a change occurred and over what duration. Regular records support comparison.

The direct answer is A. Qualitative data describe qualities in words, such as colour, texture, or smell. Quantitative data are numerical measurements or counts, such as height, mass, or number. In option A, leaf colour is qualitative because it may be described as light green or dark green, while leaf number is quantitative because it can be counted. A fertiliser experiment can therefore record both kinds of evidence. Option B records only beauty, which is a subjective qualitative opinion and provides no quantitative value. Option C records only taste, also qualitative and potentially unsuitable for a safe plant experiment; it has no number. Option D gives only an opinion, so it is neither a reliable numerical measurement nor a well-defined observation. A is correct. Memory cue: “quality” means descriptive words; “quantity” means numbers or counts.

Honest data recording means writing down every observation and result, whether it matches the expectation or not. Therefore, option A is correct. Recording only favourable results or selecting data to fit a conclusion introduces bias. Unusual values should be recorded and investigated for possible causes rather than altered. Exam tip: In scientific experiments, raw data should be recorded completely and without changing it.

Similar results obtained by different students using the same method show reproducibility. The procedure should be clear.

A hypothesis is a testable possible explanation and an experiment provides data for or against it. The conclusion is based on evidence.

Keeping water temperature constant makes the effect of stirring speed clear. Keep controlled factors the same in every trial.

More samples can make the result more representative and reliable. Avoid broad conclusions from a small sample.